Breast Cancer Detection Rate Research Articles

Cost-effectiveness analysis of breast cancer screening by mammography in average-risk women in China

Breast cancer is the most common form of cancer among women worldwide and is often at an advanced stage when clinical diagnosis. Fortunately, mammography could increase the detection rate of early-stage breast cancer. In this study, we conducted a cost-effectiveness analysis of breast cancer screening compared with no screening for average-risk women aged 45-69 every two years in China. Parameters about screening, costs, and utilities were taken from previous studies. Compared with no screening, mammography screening cost $1566.12 more with a reduction of 0.0088 Quality-Adjusted Life Years (QALYs), which was absolutely dominated. In summary, breast cancer screening by mammography was more recommended to high-risk women rather than average-risk women in China.

Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis.

Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.

The effects of breast density on the benefits of mammograms with adjunctive ultrasonography in breast screening.

Various efforts have been made to improve the accuracy of breast cancer screening. This study aimed to report differences in the contribution of ultrasonography to cancer screening assessments of dense and non-dense breasts. The participants in this study were 29,640 Japanese women in their 40s who underwent breast cancer screening at the Iwate Cancer Society between 2018 and 2021. This included women who chose mammography alone or mammography with adjunctive ultrasonography (overall assessment). They were classified into two groups according to the breast density in mammography: dense breasts and non-dense breasts. Recall rate, breast cancer detection rate, and positive predictive value of the two screening-type groups were evaluated for each breast density group. Of the 29,640 women analyzed, 18,861 (63.6%) underwent mammography alone and 10,779 (36.3%) were by overall assessments. The number of women recalled was higher in the overall assessment group than in the mammography-alone group (2.9% vs. 1.9%, p < 0.01). The proportion of women in whom breast cancer was detected was higher in the overall assessment group than in the mammography-alone group (0.31% [n = 33] vs. 0.15% [n = 28], p < 0.01). For non-dense breasts, there were no significant differences in either the recall rate or the breast cancer detection rate between those who underwent mammography alone and those who underwent overall assessment. Conversely, for dense breasts, the recall rate after mammography alone was lower than that after overall assessment (1.8% vs. 3.8%, p < 0.01), and the breast cancer detection rate was higher after overall assessment than after mammography alone (0.40% vs. 0.18%, p < 0.01). We found the benefits of adjunctive ultrasonography with mammography to differ depending on breast density. This could be used to tailor the selection of screening modalities to individuals.

Impact of real-life use of artificial intelligence as support for human reading in a population-based breast cancer screening program with mammography and tomosynthesis

ObjectivesTo evaluate the impact of using an artificial intelligence (AI) system as support for human double reading in a real-life scenario of a breast cancer screening program with digital mammography (DM) or digital breast tomosynthesis (DBT).Material and methodsWe analyzed the performance of double reading screening with mammography and tomosynthesis after implementarion of AI as decision support. The study group consisted of a consecutive cohort of 1 year screening between March 2021 and March 2022 where double reading was performed with concurrent AI support that automatically detects and highlights lesions suspicious of breast cancer in mammography and tomosynthesis. Screening performance was measured as cancer detection rate (CDR), recall rate (RR), and positive predictive value (PPV) of recalls. Performance in the study group was compared using a McNemar test to a control group that included a screening cohort of the same size, recorded just prior to the implementation of AI.ResultsA total of 11,998 women (mean age 57.59 years ± 5.8 [sd]) were included in the study group (5049 DM and 6949 DBT). Comparing global results (including DM and DBT) of double reading with vs. without AI support, we observed an increase in CDR, PPV, and RR by 3.2/‰ (5.8 vs. 9; p < 0.001), 4% (10.6 vs. 14.6; p < 0.001), and 0.7% (5.4 vs. 6.1; p < 0.001) respectively.ConclusionAI used as support for human double reading in a real-life breast cancer screening program with DM and DBT increases CDR and PPV of the recalled women.Clinical relevance statementArtificial intelligence as support for human double reading improves accuracy in a real-life breast cancer screening program both in digital mammography and digital breast tomosynthesis.Key Points• AI systems based on deep learning technology offer potential for improving breast cancer screening programs.• Using artificial intelligence as support for reading improves radiologists’ performance in breast cancer screening programs with mammography or tomosynthesis.• Artificial intelligence used concurrently with human reading in clinical screening practice increases breast cancer detection rate and positive predictive value of the recalled women.

Uptake of screening MRI in high risk patients without a personal history of breast cancer.

133 Background: The American Cancer Society (ACS) recommends breast MRI in addition to annual screening mammogram for women who are high risk for breast cancer starting at age 30, regardless of breast density. This includes women with a lifetime risk of breast cancer of 20% or greater, according to breast cancer risk assessment tools. In this study, we evaluated the uptake of supplemental MRI screening in patients ages 25-75 classified as high risk (Tyrer Cuzick (TC) score greater than 20%), without a personal history of breast cancer (PHBC). Methods: Cancer risk assessment software was used to calculate a TC score. Data was extracted from the EMR of an academic center for patients ages 25-75 years old between 07/20/2020 and 07/29/2021 with a TC score of ≥ 20%. Factors such as age, ethnicity, race, insurance type, breast density, and whether patients had access to a High-Risk Breast Cancer (HRBC) provider were analyzed and a chi-squared test was used to determine whether there were significant differences in MRI supplemental uptake based on these subsets. Results: 1144 patients were identified as high risk, having a TC ≥ 20%. 27% (n=307) underwent supplemental MRI screening, while 73% (n=837) did not. 842 patients were classified as having dense breasts and 28% (n=237) of these patients underwent MRI. Of 635 patients with an MRI order, 36% (n=227) had access to a HRBC provider, while 64% (n=408) did not. Patients with a HRBC provider were more likely to fulfill their MRI order (88%, n=200), compared to patients without a HRBC provider (33%, n=135) (p&lt;0.001). Conclusions: Various factors may be influencing whether high risk patients complete supplemental screening MRI. Patients with dense breasts and those with access to HRBC were more likely to undergo supplemental MRI screening. Patients who have not completed their recommended breast MRI may be faced with various challenges, such as, not having access to a HRBC provider. Identifying barriers to screening can help increase early rates of breast cancer detection in this high risk population of women.

A multicentre review of the direct-access mammography programme in Ireland for women with breast pain

To assess the current use of the direct access mammography pathway for breast pain and the rate of breast cancer detection in this patient cohort. A retrospective review of general practitioner (GP)-referred mammograms performed during a 12-month period from January to December 2022 across four tertiary referral centres. With the use of medical records and GP referrals, patient demographics, presenting symptoms, family history, and clinical outcomes were recorded. The present study comprised 2,046 patients of which 21.6% did not report breast pain at the time of referral. Thirty-five per cent had a positive family history with 40% of these patients having no breast pain. Twelve per cent were recalled with 30% of these patients requiring biopsy. An overall cancer detection rate (CDR) of 7 per 1000 was determined for women with mastalgia. A CDR of 0 per 1,000 was determined for women <50 years with mastalgia alone and no additional risk factors for malignancy. Fisher's exact test showed no statistically significant association between breast pain and breast cancer. There was no statistically significant relationship found between breast pain and breast cancer. This review suggests a low cancer detection rate in women <50 years. In women <50 years with mastalgia without additional symptoms or family history, breast imaging is not required.

Application of Raman spectroscopy in breast cancer surgery

The incidence of breast cancer is increasing worldwide. As cancer screening has become more widespread, the rate of early breast cancer detection has increased and treatment methods have changed. Partial mastectomy is performed more often than total mastectomy for the surgical treatment of early breast cancer, and sentinel lymph node biopsy plays an important role. A high level of accuracy is necessary for the intraoperative examination of surgical margins and sentinel lymph nodes to identify malignancies. Therefore, several examination techniques, including Raman spectroscopy, that replace or supplement the currently used frozen-section methods are being studied. Raman spectroscopy has the ability to diagnose cancer in normal tissue by providing in real time a chemical fingerprint that can be used to differentiate between cells and tissues. Numerous studies have investigated the utilization of Raman spectroscopy to identify cancer in the margins of resected tissues and sentinel lymph nodes during breast cancer surgery, showing the potential of this technique for clinical applications. This article introduces and reviews the research on Raman spectroscopy for breast cancer surgery.

Novel Technologies in Breast Imaging: A Scoping Review.

Breast cancer is one of the leading causes of death in the United States and can cause considerable suffering for not only the patient but their families as well. The current mainstay of screening is mammography, although this screening modality has its drawbacks. Multiple technologies have been recently explored in hopes of increasing breast cancer detection rates and decreasing false positive rates. Overall, improving breast cancer screening techniques has the potential to decrease cost, patient anxiety, and the use of unnecessary procedures. This review discusses multiple modalities including digital breast tomosynthesis, contrast-enhanced dual-energy digital mammography (CE DE DM), MRI with diffusion-weighted sequences and proton magnetic resonance spectroscopy. This paper was written with the objective of synthesizing information across several databasesto provide clinicians with a more accessible tool to understand the underlying concepts behind these imaging modalities, as well as present reviewed data which highlights the benefits and drawbacks of these breast cancer-detecting techniques.

Evaluating a novel patient pathway to manage symptomatic breast referrals (the blue flag clinic): a longitudinal observational study.

A novel referral pathway for exhibited breast symptom (EBS) referrals to manage increasing referrals of urgent suspected cancer (USC) was implemented in our trust. We report on the safety and effect on compliance with the 2-week-wait rule (2WW). A single-centre longitudinal observational study included all patients referred to a UK breast unit during 13 May 2019 to 27 March 2020 (period 1) and 8 February 2021 to 31 January 2022 (period 2). USC referrals were assessed in a one-stop clinic (red flag clinic [RFC]); EBS referrals were assessed in a new clinic in which clinical evaluation was performed and imaging occurred subsequently (blue flag clinic [BFC]). Patients were followed up to determine the symptomatic interval cancer rate. There were 9,695 referrals; 1,655 referrals (17%) were assessed in the BFC after 63 exclusions. Some 95.9% of patients had a benign clinical examination (P1/P2), 80.1% had imaging (mammogram or ultrasound) and 4% had a tissue biopsy. In total, 16/1,655 (0.97%) BFC patients and 510/7,977 (8.2%) RFC patients were diagnosed with breast cancer (breast cancer detection rate). Some 1,631 patients (with 1,639 referrals) were discharged and followed up for a median of 17 months (interquartile range 12-32) with one subsequent cancer diagnosis (symptomatic interval cancer rate, 0.06%). Implementation of the BFC pathway increased 3-month average trust performance of USC referrals with 2WW standard from 8.5% to 98.7% (period 1) and from 30% to 66% (period 2). The BFC pathway for EBS patients is safe and implementation led to improvement against the 2WW target for USC referrals, ensuring resources are prioritised to patients with the highest likelihood of breast cancer.

Evaluation of a population-based breast cancer screening in North China

BackgroundDespite mammography-based screening for breast cancer has been conducted in many countries, there are still little data on participation and diagnostic yield in population-based breast cancer screening in China.MethodsWe enrolled 151,973 eligible women from four cities in Hebei Province within the period 2013–2021 and followed up until December 31, 2021. Participants aged 40–74 who assessed as high risk were invited to undergo breast ultrasound and mammography examination. Overall and group-specific participation rates were calculated. Multivariable analyses were used to estimate the factors associated with participation rates. The diagnostic yield of both screening and no screening groups was calculated. We further analyzed the stage distribution and molecular subtype of breast cancer cases by different modes of cancer detection.ResultsA total of 42,547 participants were evaluated to be high risk of breast cancer. Among them, 23,009 subjects undertook screening services, with participation rate of 54.08%. Multivariable logistic regression model showed that aged 45–64, high education level, postmenopausal, current smoking, alcohol consumption, family history of breast cancer, and benign breast disease were associated with increased participation of screening. After median follow-up of 3.79 years, there were 456 breast cancer diagnoses of which 65 were screen-detected breast cancers (SBCs), 27 were interval breast cancers (IBCs), 68 were no screening cancers, and 296 were cancers detected outside the screening program. Among them, 92 participants in the screening group (0.40%) and 364 in the non-screening group (0.28%) had breast cancer detected, which resulted in an odds ratio of 1.42 (95% CI 1.13–1.78; P = 0.003). We observed a higher detection rate of breast cancer in the screening group, with ORs of 2.42 (95% CI 1.72–3.41) for early stage (stages 0–I) and 2.12 (95% CI 1.26–3.54) for luminal A subtype. SBCs had higher proportion of early stage (71.93%) and luminal A subtype (47.22%) than other groups.ConclusionsThe significant differences in breast cancer diagnosis between the screening and non-screening group imply an urgent need for increased breast cancer awareness and early detection in China.

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study.

Early diagnosis of breast cancer has always been a difficult clinical challenge. We developed a deep-learning model EDL-BC to discriminate early breast cancer with ultrasound (US) benign findings. This study aimed to investigate how the EDL-BC model could help radiologists improve the detection rate of early breast cancer while reducing misdiagnosis. In this retrospective, multicentre cohort study, we developed an ensemble deep learning model called EDL-BC based on deep convolutional neural networks. The EDL-BC model was trained and internally validated on B-mode and color Doppler US image of 7955 lesions from 6795 patients between January 1, 2015 and December 31, 2021 in the First Affiliated Hospital of Army Medical University (SW), Chongqing, China. The model was assessed by internal and external validations, and outperformed radiologists. The model performance was validated in two independent external validation cohorts included 448 lesions from 391 patients between January 1 to December 31, 2021 in the Tangshan People's Hospital (TS), Chongqing, China, and 245 lesions from 235 patients between January 1 to December 31, 2021 in the Dazu People's Hospital (DZ), Chongqing, China. All lesions in the training and total validation cohort were US benign findings during screening and biopsy-confirmed malignant, benign, and benign with 3-year follow-up records. Six radiologists performed the clinical diagnostic performance of EDL-BC, and six radiologists independently reviewed the retrospective datasets on a web-based rating platform. The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and two independent external validation cohorts for EDL-BC was 0.950 (95% confidence interval [CI]: 0.909-0.969), 0.956 (95% [CI]: 0.939-0.971), and 0.907 (95% [CI]: 0.877-0.938), respectively. The sensitivity values were 94.4% (95% [CI]: 72.7%-99.9%), 100% (95% [CI]: 69.2%-100%), and 80% (95% [CI]: 28.4%-99.5%), respectively, at 0.76. The AUC for accurate diagnosis of EDL-BC (0.945 [95% [CI]: 0.933-0.965]) and radiologists with artificial intelligence (AI) assistance (0.899 [95% [CI]: 0.883-0.913]) was significantly higher than that of the radiologists without AI assistance (0.716 [95% [CI]: 0.693-0.738]; p<0.0001). Furthermore, there were no significant differences between the EDL-BC model and radiologists with AI assistance (p=0.099). EDL-BC can identify subtle but informative elements on US images of breast lesions and can significantly improve radiologists' diagnostic performance for identifying patients with early breast cancer and benefiting the clinical practice. The National Key R&D Program of China.

Impact of the systematic introduction of tomosynthesis on breast biopsies: 10 years of results

Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori “Giovanni Paolo II” of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute’s Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs.

Advances in the application of cryotherapy to the treatment of breast cancer

Abstract Cryotherapy is a minimally invasive treatment. With increasing rate of breast cancer detection, ablation therapy has become an alternative approach for the treatment of this pathology due to its advantages of minimally invasive, fewer complications, good efficacy, and repeatability. Preclinical and clinical studies of cryotherapy for breast cancer published through December 2022 were identified through a literature search using PubMed, ClinicalTrials.gov, and CNKI. This article reviews the progress of cryoablation in the treatment of breast cancer.

Preoperative staging by multimodal imaging in newly diagnosed breast cancer: Diagnostic performance of contrast-enhanced spectral mammography compared to conventional mammography, ultrasound, and MRI

PurposeTo compare contrast-enhanced spectral mammography (CESM) with mammography (Mx), ultrasound (US), and magnetic resonance imaging (MRI) regarding breast cancer detection rate and preoperative local staging. Material and methodsThis prospective observational, single-centre study included 128 female patients (mean age 55.8 ± 11.5 years) with a newly diagnosed malignant breast tumour during routine US and Mx were prospectively enrolled. CESM and MRI examinations were performed within the study. Analysis included interreader agreement, tumour type and grade distribution, detection rates (DR), imaging morphology, contrast-enhancement and was performed by two independent readers blinded to patient history and histopathological diagnosis. Assessment of local disease extent was compared between modalities via Bland-Altman plots. ResultsOne-hundred-and-ten tumours were classified as NST (85.9%), 4 as ILC (3.1%) and 10 as DCIS (7.8%). DR was highest for MRI (128/128, 100.0%), followed by US (124/128, 96.9%) and CESM (123/128, 96.1%) and lowest for conventional Mx (106/128, 82.8%) (p = 0.0002). Higher breast density did not negatively affect DR of US, CESM or MRI. Local tumour extent measurements based on CESM (Bland-Altman bias 6.6, standard deviation 30.2) showed comparable estimation results to MRI, surpassing Mx (23.4/43.7) and US (35.4/40.5). Even though detection of multifocality and multicentricity was highest for CESM and MRI (p < 0.0001), second-look rates, i.e., targeted US examinations after MRI or CESM, were significantly lower for CESM (10.2% of cases) compared to MRI (16.2%) with a significantly higher true positive rate for CESM (72.0%) vs. MRI (42.5%). ConclusionCESM is a viable alternative to MRI for lesion detection and local staging in newly diagnosed malignant breast cancer and provides higher specificity in regard to second-look examinations.

Magnetic resonance imaging in screening women at high risk of breast cancer: A meta-analysis.

Magnetic resonance imaging (MRI) is more accurate than mammography in screening for breast cancer. Exposure to ionizing radiation from repeated diagnostic X-rays may be a cause of breast cancer. We conducted systematic searches on PubMed, Cochrane and Embase to identify studies on women who underwent mammography or MRI screening. A meta-analysis was performed to compare the detection rate of breast cancer by mammography, MRI or both. A total of 18 diagnostic publications were identified and included in the meta-analysis. Among the 1000 screened women, MRI alone increased the detection rate of breast cancer by 8 compared with mammography alone (RR 0.48, 95% CI 0.42-0.54), and MRI plus mammography increased the detection rate of breast cancer by 1 compared with MRI alone (RR 0.86, 95% CI 0.78-0.96). Subgroup analysis demonstrated that the diagnostic efficacy of MRI plus mammography in breast was obviously better than that of MRI alone or mammography alone. Screening with MRI alone might be the best choice for women at high risk of breast cancer.

The dual-crosslinked prospective values of RAI14 for the diagnosis and chemosurveillance in triple negative breast cancer

Objective The exploration of non-invasive biomarkers for assessing tumor response is critical to optimize treatment decisions. In this study, we aimed at determining the potential role of RAI14 in the early diagnosis and evaluation of chemotherapy efficacy in triple-negative breast cancer (TNBC). Methods We recruited 116 patients newly diagnosed with breast cancer, 30 patients with benign breast disease and 30 healthy controls. In addition, 57 TNBC patients were collected in serum at different time points (C0, C2 and C4) for chemotherapy monitoring. The expression of serum RAI14 and CA15-3 were quantified by Elisa and electrochemiluminescence assay, respectively. Then we compared the performances of markers with the chemotherapy efficacy assessed by imaging. Results RAI14 is significantly overexpressed in TNBC and is linked to adverse clinicopathological features such as tumor burden, CA15-3 levels and the ER, PR, and HER2 status of the patients. ROC curve analysis showed that RAI14 improves the diagnostic performance for CA15-3(AUCRAI14 = 0.934 vs. AUCCA15-3 = 0.836), especially embodied in early-stage breast cancer diagnosis and patients with CA15-3 negativity. Furthermore, RAI14 behaves well in reproducing treatment response which was consistent with clinical Imaging assessment. Conclusions Recent studies showed that RAI14 has a complementary effect to CA15-3 and a test combining the two parameters can improve the detection rate of early triple-negative breast cancer. At the same time, RAI14 plays a more important role in chemotherapy monitoring than CA15-3 as the change in its concentration is in line with the tumor volume variation. Taken together, RAI14 is a reliable novel marker in the early diagnosis and chemotherapy monitoring of triple-negative breast cancer.

Cost-effectiveness evaluation of risk-based breast cancer screening in Urban Hebei Province

To evaluate the implementations of Cancer Screening Program in Urban Hebei and to model the cost-effectiveness of a risk-based breast Cancer Screening Program. Women aged 40–74 years were invited to participate the Cancer Screening Program in Urban Hebei form 2016 to 2020 by completing questionnaires to collect information about breast cancer exposure. Clinical screening including ultrasound and mammography examination were performed. We developed a Markov model to estimate the lifetime costs and benefits, in terms of quality-adjusted life years (QALY), of a high-risk breast Cancer Screening Program. Nine screening strategies and no screening were included in the study. The age-specific incidence, transition probability data and lifetime treatment costs were derived and adopted from other researches. Average cost-effectiveness ratios (ACERs) were estimated as the ratios of the additional costs of the screening strategies to the QLYG compared to no screening. Incremental cost-effectiveness ratios (ICERs) were calculated based on the comparison of a lower cost strategies to the next more expensive and effective strategies after excluding dominated strategies and extendedly dominated strategies. ICERs were used to compare with a willingness-to-pay (WTP) threshold. Sensitivity analysis was explored the influence factors. A total of 84,029 women completed a risk assessment questionnaire, from which 20,655 high-risk breast cancer females were evaluated, with a high-risk rate of 24.58%. There were 13,392 high-risk females completed the screening program, with participation rate was 64.84%. Undergoing ultrasound, mammography and combined screening, the suspicious positive detection rates were 15.00%, 9.20% and 19.30%, and the positive detection rates were 2.11%, 2.76% and 3.83%, respectively. According to the results by Markov model, at the end of 45 cycle, the early diagnosis rates were 55.53%, 60.68% and 62.47% underwent the annual screening by ultrasound, mammography and combined, the proportion of advanced cancer were 17.20%, 15.85% and 15.36%, respectively. Different screening method and interval yield varied. In the exploration of various scenarios, annual ultrasound screening is the most cost-effective strategy with the ICER of ¥116,176.15/QALY. Sensitivity analyses demonstrated that the results are robust. Although it was not cost effective, combined ultrasound and mammography screening was an effective strategy for higher positive detection rate of breast cancer. High-risk population-based breast cancer screening by ultrasound annually was the most cost-effective strategy in Urban Hebei Province.

Breast cancer detection and diagnosis using hybrid deep learning architecture

The detection of cancer regions in the breast is important to save the women patient’s life. In this paper, mammogram image is classified into normal, benign, and malignant using the proposed hybrid Convolutional Neural Networks (CNN) architecture. The proposed system consists of a radon transform, data augmentation module, and hybrid CNN architecture. The radon transform transforms each spatial pixel in the source mammogram image into a time–frequency variation image. This image is data augmented to construct a new dataset from the existing dataset to improve the breast cancer detection rate. The data augmented images are classified into three different cases using the proposed hybrid CNN architecture. Further, a mathematical morphological-based segmentation algorithm is used to segment the cancer pixels. In this article, Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets for mammogram image analysis are used to estimate the performance efficiency of the developed deep learning architecture. The developed CNN architecture provides 97.91% Se, 97.83% Sp, 98.44% Acc, and 98.57% JI on the mammogram images available in the DDSM dataset. The developed CNN architecture provides 98% Se, 98.66% Sp, 99.17% Acc, and 98.07% JI on the mammogram images available in the MIAS dataset. For both open access datasets, the experimental results are compared to recent similar works. From the extensive analysis of experimental results of the proposed method, the methodologies presented in this article clearly segment the boundary of the cancer region in an abnormal mammogram image.

Outcome of Screening Mammography for Women Attending Primary Healthcare Centers: One-Year Review

Background: Breast cancer is the most prevalent cancer in women globally and in the Kingdom of Bahrain. Studies have shown that screening mammography is an effective way to identify early stages of breast cancer in women and has decreased breast cancer mortality. Therefore, determining the outcome of breast cancer screening is essential for proper assessment and improvement of the screening program. Objective: This study aims to assess the outcome of breast cancer screening by mammography for women attending Primary Healthcare Centers in the Kingdom of Bahrain. Methods: This is a cross-sectional study of women who had screening mammograms in primary healthcare centers in the Kingdom of Bahrain for one year. The study included 2438 women aged 40 years and older. Data were extracted from electronic medical records. Results: The most prevalent breast density level was scattered fibroglandular density (55.3%), while extremely dense breasts (5.3%) were the least common. The study showed that 68.3% of the reports were normal, while 31.7% of women were recalled for further imaging. The most common reasons for recalls were nodules/lumps in 34.2%. Additional imaging was done for 69.4% of the cases, while 30.6% of women missed follow-up appointments. Of the patients needing biopsies, the procedure was performed for 62.5% of the women; of these, there were histologically confirmed malignancies in 31.6%. Conclusion: The breast cancer detection rate in this study is 0.5%. A recall rate of about 32% is higher than in other studies. This study also shows a high percentage of women who didn’t follow up for the additional images and procedures requested by radiologists, which needs further studies to identify the reasons. Educating women on the importance of compliance with the additionally requested workups and close monitoring and follow-up of the screened women by health facilities are crucial for the success of the breast screening program.

Application of Support Vector Machines for Breast Calcification Cluster Detection and Mass Classification

Breast cancer has become “the most common cancer worldwide”, and it is an urgent problem in the medical field to improve the detection rate of breast cancer through early diagnosis and treatment. Based on the current research on medical image processing, our work selects breast images from the MIAS database for pre-processing, segmentation, texture feature extraction and classification, and conducts an in-depth investigation and experimental validation on the theoretical basis of support vector machine algorithm, kernel function selection, and key parameter optimization. The results show the effectiveness of support vector machines in detecting abnormal areas and classifying masses in breast images and provide research ideas for breast cancer diagnosis.